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概率性扩散张量成像揭示抽动秽语综合征儿童结构网络的拓扑组织紊乱

Disrupted topological organization of structural networks revealed by probabilistic diffusion tractography in Tourette syndrome children.

作者信息

Wen Hongwei, Liu Yue, Rekik Islem, Wang Shengpei, Zhang Jishui, Zhang Yue, Peng Yun, He Huiguang

机构信息

Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

University of Chinese Academy of Sciences, Beijing, China.

出版信息

Hum Brain Mapp. 2017 Aug;38(8):3988-4008. doi: 10.1002/hbm.23643. Epub 2017 May 5.

Abstract

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. Although previous TS studies revealed structural abnormalities in distinct corticobasal ganglia circuits, the topological alterations of the whole-brain white matter (WM) structural networks remain poorly understood. Here, we used diffusion MRI probabilistic tractography and graph theoretical analysis to investigate the topological organization of WM networks in 44 drug-naive TS children and 41 age- and gender-matched healthy children. The WM networks were constructed by estimating inter-regional connectivity probability and the topological properties were characterized using graph theory. We found that both TS and control groups showed an efficient small-world organization in WM networks. However, compared to controls, TS children exhibited decreased global and local efficiency, increased shortest path length and small worldness, indicating a disrupted balance between local specialization and global integration in structural networks. Although both TS and control groups showed highly similar hub distributions, TS children exhibited significant decreased nodal efficiency, mainly distributed in the default mode, language, visual, and sensorimotor systems. Furthermore, two separate networks showing significantly decreased connectivity in TS group were identified using network-based statistical (NBS) analysis, primarily composed of the parieto-occipital cortex, precuneus, and paracentral lobule. Importantly, we combined support vector machine and multiple kernel learning frameworks to fuse multiple levels of network topological features for classification of individuals, achieving high accuracy of 86.47%. Together, our study revealed the disrupted topological organization of structural networks related to pathophysiology of TS, and the discriminative topological features for classification are potential quantitative neuroimaging biomarkers for clinical TS diagnosis. Hum Brain Mapp 38:3988-4008, 2017. © 2017 Wiley Periodicals, Inc.

摘要

抽动秽语综合征(TS)是一种起病于儿童期的神经行为障碍。尽管先前关于TS的研究揭示了不同皮质基底神经节回路中的结构异常,但全脑白质(WM)结构网络的拓扑改变仍知之甚少。在此,我们使用扩散磁共振成像概率纤维束成像和图论分析,来研究44名未用药的TS儿童以及41名年龄和性别匹配的健康儿童的WM网络拓扑组织。通过估计区域间连接概率构建WM网络,并使用图论对拓扑特性进行表征。我们发现TS组和对照组在WM网络中均表现出高效的小世界组织。然而,与对照组相比,TS儿童的全局和局部效率降低,最短路径长度和小世界特性增加,表明结构网络中局部专业化和全局整合之间的平衡被破坏。尽管TS组和对照组的枢纽分布高度相似,但TS儿童的节点效率显著降低,主要分布在默认模式、语言、视觉和感觉运动系统中。此外,使用基于网络的统计(NBS)分析确定了TS组中两个连接性显著降低的独立网络,主要由顶枕叶皮质、楔前叶和中央旁小叶组成。重要的是,我们结合支持向量机和多核学习框架,融合多个层次的网络拓扑特征对个体进行分类,准确率高达86.47%。总之,我们的研究揭示了与TS病理生理学相关的结构网络拓扑组织破坏,且用于分类的判别性拓扑特征是临床TS诊断潜在的定量神经影像学生物标志物。《人类脑图谱》38:3988 - 4008, 2017。© 2017威利期刊公司。

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